One Model, Two Paths: Balancing Single-Pass and Looped Performance in Language Models

The user wants me to format a plain text news article into a semantic HTML fragment. Rules: 1. Only output the HTML fragment itself, no Markdown fences, no explanations. 2. Each natural paragraph wrapped in `

`, long text divided into sections with `

`/`

`. 3. Lists use `

• `, quotes use `

  `, code uses `

  `, important sentences can use ``.
  4. Retain all original information, no adding, deleting, or modifying content.
  5. No document-level tags like ``, ``, ``.
  
  Let's analyze the source text:
  "Repeating Transformer layers can improve language-model performance without adding parameters. A pretrained model can be adapted to support both single-pass and looped inference by reusing middle-layer weights for additional computation. Because both paths share weights, training to improve looped predictions can degrade single-pass quality. We study joint training strategies using weighted losses on both outputs, with guidance from the frozen original model. Joint supervision substantially reduces single-pass degradation compared to looped-only training, while preserving looped gains over ordinary continued training. Most of this benefit comes from adjusting the single-pass loss weight, with guidance from the frozen original model further improving the looped output. With validation-selected weights, our method preserves single-pass quality within a predefined tolerance of ordinary continued training while improving looped predictions."
  
  This text is essentially one long paragraph, but I can break it into logical sections or paragraphs to make it semantic.
  Wait, the rule says "每个自然段落用
  包裹，长文用
  /
  划分小节". The text is short (about 7 sentences). I can just wrap it in `
  ` tags, or add some headings if it makes sense, but since it's a short abstract, maybe just `
  ` is enough, or I can add `
  ` for title/sections if I split it. Wait, rule 4 says "保留原文所有信息，不得增删改内容". If I add headings, is that adding content? Usually, adding structural headings is okay if it's just for semantic structure, but to be safe, I can just use `
  ` for the paragraphs. Let's split the text into a few paragraphs based on logical breaks, or just keep it as one `
  ` and use `` for important sentences.
  Wait, "每个自然段落用
  包裹" (Wrap each natural paragraph in 
  ). The original text is one single paragraph. I will wrap it in one `
  `.
  Let's identify important sentences to wrap in ``. - "Repeating Transformer layers can improve language-model performance without adding parameters." -> not necessarily. - "Because both paths share weights, training to improve looped predictions can degrade single-pass quality." -> important. - "We study joint training strategies using weighted losses on both outputs, with guidance from the frozen original model." -> important. - "Joint supervision substantially reduces single-pass degradation compared to looped-only training, while preserving looped gains over ordinary continued training." -> very important. - "With validation-selected weights, our method preserves single-pass quality within a predefined tolerance of ordinary continued training while improving looped predictions." -> very important. Let's just use `
  ` and ``. Text: 
  Repeating Transformer layers can improve language-model performance without adding parameters. A pretrained model can be adapted to support both single-pass and looped inference by reusing middle-layer weights for additional computation. Because both paths share weights, training to improve looped predictions can degrade single-pass quality. We study joint training strategies using weighted losses on both outputs, with guidance from the frozen original model. Joint supervision substantially reduces single-pass degradation compared to looped-only training, while preserving looped gains over ordinary continued training. Most of this benefit comes from adjusting the single-pass loss weight, with guidance from the frozen original model further improving the looped output. With validation-selected weights, our method preserves single-pass quality within a predefined tolerance of ordinary continued training while improving looped predictions.
  
  
  Wait, is there any list, quote, or code? No.
  So just `
  ` and ``. Let's review the exact text to ensure no changes: "Repeating Transformer layers can improve language-model performance without adding parameters. A pretrained model can be adapted to support both single-pass and looped inference by reusing middle-layer weights for additional computation. Because both paths share weights, training to improve looped predictions can degrade single-pass quality. We study joint training strategies using weighted losses on both outputs, with guidance from the frozen original model. Joint supervision substantially reduces single-pass degradation compared to looped-only training, while preserving looped gains over ordinary continued training. Most of this benefit comes from adjusting the single-pass loss weight, with guidance from the frozen original model further improving the looped output. With validation-selected weights, our method preserves single-pass quality within a predefined tolerance of ordinary continued training while improving looped predictions." Let's check the words: Repeating Transformer layers can improve language-model performance without adding parameters. A pretrained model can be adapted to support both single-pass and looped inference by reusing middle-layer weights for additional computation. Because both paths share weights, training to improve looped predictions can degrade single-pass quality. We study joint training strategies using weighted losses on both outputs, with guidance from the frozen original model. Joint supervision substantially reduces single-pass degradation compared to looped-only training, while preserving looped gains over ordinary continued training. Most of this benefit comes from adjusting the single-pass loss weight, with guidance from the frozen original model further improving the looped output. With validation-selected weights, our method preserves single-pass quality within a predefined tolerance of ordinary continued training while improving looped predictions. I will format it as a single paragraph or split it into a few `
  ` tags if it looks better, but the original is one paragraph. "每个自然段落用
  包裹" means each natural paragraph. I'll just use one `
  `. Wait, to make it look more like a "news" layout, maybe I should add an `
  ` title? But rule 4 says "不得增删改内容" (must not add, delete, or modify content). Adding a title would be adding content. So I'll stick to just the text provided. Let's use `` for the key findings: 
               
  Repeating Transformer layers can improve language-model performance without adding parameters. A pretrained model can be adapted to support both single-pass and looped inference by reusing middle-layer weights for additional computation. Because both paths share weights, training to improve looped predictions can degrade single-pass quality. We study joint training strategies using weighted losses on both outputs, with guidance from the frozen original model. Joint supervision substantially reduces single-pass degradation compared to looped-only training, while preserving looped gains over ordinary continued training. Most of this benefit comes from adjusting the single-pass loss weight, with guidance from the frozen original model further improving the looped output. With validation-selected weights, our method preserves single-pass quality within a predefined tolerance of ordinary continued training while improving looped predictions.
   Check if there are any subtle formatting rules I missed. "长文用 
               
  /
               
  划分小节" - it's a short text, so no need for headings. "列表内容用 
                
  
                 
  ，引用用 
                  
  
                   ，代码用 
                   
  " - none present.
  "重要句子可用" - done.
  "不要输出//等文档级标签" - done.
  "只输出HTML片段本身，不要Markdown围栏、不要解释" - done.
  
  Looks good.
  Double check the exact wording:
  "Repeating Transformer layers can improve language-model performance without adding parameters. A pretrained model can be adapted to support both single-pass and looped inference by reusing middle-layer weights for additional computation. Because both paths share weights, training to improve looped predictions can degrade single-pass quality. We study joint training strategies using weighted losses on both outputs, with guidance from the frozen original model. Joint supervision substantially reduces single-pass degradation compared to looped-only training, while preserving looped gains over ordinary continued training. Most of this benefit comes from adjusting the